Learning to Avoid Objects and Dock·with a Mobile Robot

In this paper we describe a novel robot learning method that enables a mobile robot equipped with sonar and IR light sensors to automatically acquire the ability to negotiate objects and dock by simply interacting with the environment. We achieve this by providing the robot with sonar and IR sensors for detecting objects and the relative direction of IR beacons placed in the environment. A set of fuzzy associative maps (FAMs) is also provided to the robot for learning associations between sonar sensor data, immediate trajectories and appropriate velocities for traversing trajectories. Learning is performed in real time without the credit assignment problem by training each FAM with training data acquired from sonar sensors and the robot's interactions with the environment. Once the robot learns to adequately perceive its environment in terms of trajectory velocities, object aviodance, and docking behaviour is possible by providing the robot with a single instruction to: follow fast trajectories toward highest priority beacons. Results are provided that show how this learning approach can automatically enable a mobile robot to acquire navigation skills and the ability to locate and dock with its charging bay within cluttered environments.